Hands-on component of Single-cell RNA-seq workshop: Sessions 1-2
Krishna Choudhary
3/27/2021
Introduction
We'll review the current practices for some of the common steps in scRNA-seq data analysis in the Sessions 1-2. We'll discuss different practices for each step and the assumptions underlying various tools and their limitations. This document provides an exposure to one of the popular tools for such analysis. As we'll discuss, the right choice of method in any application depends on a number of factors, including the biological systems under study and the characteristics of the data in hand. For the purpose of our workshop, we'll limit the hands-on component to the Seurat package in R. In general, analysis might require multiple tools in different languages and/or novel development. For more, please see the slide deck in materials.
The following is based on this vignette from the Seurat developers.
Setup the working environment
library(dplyr)
library(Seurat)
library(patchwork)Load the data
Please ensure that the directory named "pbmc3k_data" in the workshop materials is in the same directory as this .Rmd file.
data <- CreateSeuratObject(counts = Read10X("pbmc3k_data"),
project = "Hello_scWorld", #Name this whatever.
min.cells = 3, # Don't keep genes observed in fewer than 3 cells
min.features = 200) # Don't keep cells with fewer than 200 genes## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')Filter poor quality or uninteresting cells
#Assess percent of mitochondrial counts in each cell
data[["percent_mt"]] <- PercentageFeatureSet(object = data,
pattern = "^MT-")
#Violin plot
VlnPlot(object = data,
features = c("nFeature_RNA",
"nCount_RNA",
"percent_mt"),
ncol = 3)
#Other plotting otions
plot1 <- FeatureScatter(object = data,
feature1 = "nCount_RNA",
feature2 = "percent_mt")
plot2 <- FeatureScatter(object = data,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA")
plot1 + plot2
#Subset data
data <- subset(x = data,
subset = nFeature_RNA > 200 &
nFeature_RNA < 2500 &
percent_mt < 5)
VlnPlot(object = data,
features = c("nFeature_RNA",
"nCount_RNA",
"percent_mt"),
ncol = 3)
Normalization
data <- NormalizeData(object = data,
normalization.method = "LogNormalize",
scale.factor = 10000)Feature selection
data <- FindVariableFeatures( object = data,
selection.method = "vst",
nfeatures = 2000)
#View the 10 most highly variable genes
top10 <- head(x = VariableFeatures(object = data), 10)
print(top10)## [1] "PPBP" "LYZ" "S100A9" "IGLL5" "GNLY" "FTL" "PF4" "FTH1"
## [9] "GNG11" "S100A8"#Seurat allows plotting variable features with and without labels
plot1 <- VariableFeaturePlot(object = data)
plot2 <- LabelPoints(plot = plot1,
points = top10,
repel = TRUE)## When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 ## Warning: Transformation introduced infinite values in continuous x-axis
plot2## Warning: Transformation introduced infinite values in continuous x-axis
Dimensionality reduction
Linear dimensionality reduction
#By default Seurat only scales the variable features.
#Explicit input required to rescale all the genes
scale_genes <- rownames(data)
data <- ScaleData(object = data,
features = scale_genes)## Centering and scaling data matrix# Use the highly variable genes to find principal components
data <- RunPCA(object = data,
features = VariableFeatures(object = data),
verbose = FALSE)
#Examine and visualize PCA results a few different ways
print(x = data[["pca"]], dims = 1:5, nfeatures = 5)## PC_ 1
## Positive: CST3, TYROBP, LST1, AIF1, FTL
## Negative: MALAT1, LTB, IL32, IL7R, CD2
## PC_ 2
## Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DQB1
## Negative: NKG7, PRF1, CST7, GZMB, GZMA
## PC_ 3
## Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1
## Negative: PPBP, PF4, SDPR, SPARC, GNG11
## PC_ 4
## Positive: HLA-DQA1, CD79B, CD79A, MS4A1, HLA-DQB1
## Negative: VIM, IL7R, S100A6, IL32, S100A8
## PC_ 5
## Positive: GZMB, NKG7, S100A8, FGFBP2, GNLY
## Negative: LTB, IL7R, CKB, VIM, MS4A7DimPlot(data, reduction = "pca")
data <- JackStraw(object = data, num.replicate = 100)
data <- ScoreJackStraw(object = data, dims = 1:20)
JackStrawPlot(data, dims = 1:15)## Warning: Removed 23496 rows containing missing values (geom_point).
ElbowPlot(data)
VizDimLoadings(object = data, dims = 1:2, reduction = "pca")
Nonlinear dimensionality reduction
data <- RunUMAP(data, dims = 1:10)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 05:36:09 UMAP embedding parameters a = 0.9922 b = 1.112## 05:36:09 Read 2638 rows and found 10 numeric columns## 05:36:09 Using Annoy for neighbor search, n_neighbors = 30## 05:36:09 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 05:36:09 Writing NN index file to temp file /var/folders/7z/my8thnc51jv128q_9jfpwxb80000gp/T//RtmpiC4ILv/file641570ebcbaa
## 05:36:09 Searching Annoy index using 1 thread, search_k = 3000
## 05:36:10 Annoy recall = 100%
## 05:36:10 Commencing smooth kNN distance calibration using 1 thread
## 05:36:11 Initializing from normalized Laplacian + noise
## 05:36:11 Commencing optimization for 500 epochs, with 105124 positive edges
## 05:36:14 Optimization finisheddata <- RunTSNE(data, dims = 1:10)
DimPlot(data, reduction = "umap")
DimPlot(data, reduction = "tsne")
Clustering
data <- FindNeighbors(data, dims = 1:10)## Computing nearest neighbor graph## Computing SNNdata <- FindClusters(data, resolution = 0.5)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 2638
## Number of edges: 95965
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8723
## Number of communities: 9
## Elapsed time: 0 secondsDimPlot(data, reduction = "umap", label = TRUE) 
DimPlot(data, reduction = "tsne", label = TRUE)
DimPlot(data, reduction = "pca", label = TRUE)
Save the Seurat object
test <- data[, 1:10 ]
saveRDS(test, file = "hello_scWorld.rds")Find markers
# find all markers of cluster 1
cluster1.markers <- FindMarkers(data,
ident.1 = 1,
min.pct = 0.25)
head(cluster1.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## S100A9 0.000000e+00 5.570063 0.996 0.215 0.000000e+00
## S100A8 0.000000e+00 5.477394 0.975 0.121 0.000000e+00
## FCN1 0.000000e+00 3.394219 0.952 0.151 0.000000e+00
## LGALS2 0.000000e+00 3.800484 0.908 0.059 0.000000e+00
## CD14 2.856582e-294 2.815626 0.667 0.028 3.917516e-290# find all markers distinguishing cluster 5 from clusters 0 and 3
cluster5.markers <- FindMarkers(data,
ident.1 = 5,
ident.2 = c(0, 3),
min.pct = 0.25)
head(cluster5.markers,
n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## FCGR3A 2.150929e-209 4.267579 0.975 0.039 2.949784e-205
## IFITM3 6.103366e-199 3.877105 0.975 0.048 8.370156e-195
## CFD 8.891428e-198 3.411039 0.938 0.037 1.219370e-193
## CD68 2.374425e-194 3.014535 0.926 0.035 3.256286e-190
## RP11-290F20.3 9.308287e-191 2.722684 0.840 0.016 1.276538e-186# find markers for every cluster compared to all remaining cells, report only the positive ones
data.markers <- FindAllMarkers(data,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)## Calculating cluster 0## Calculating cluster 1## Calculating cluster 2## Calculating cluster 3## Calculating cluster 4## Calculating cluster 5## Calculating cluster 6## Calculating cluster 7## Calculating cluster 8data.markers %>%
group_by(., cluster) %>%
top_n(., n = 2, wt = avg_log2FC)## Registered S3 method overwritten by 'cli':
## method from
## print.boxx spatstat.geom## # A tibble: 18 x 7
## # Groups: cluster [9]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 1.74e-109 1.07 0.897 0.593 2.39e-105 0 LDHB
## 2 1.17e- 83 1.33 0.435 0.108 1.60e- 79 0 CCR7
## 3 0. 5.57 0.996 0.215 0. 1 S100A9
## 4 0. 5.48 0.975 0.121 0. 1 S100A8
## 5 7.99e- 87 1.28 0.981 0.644 1.10e- 82 2 LTB
## 6 2.61e- 59 1.24 0.424 0.111 3.58e- 55 2 AQP3
## 7 0. 4.31 0.936 0.041 0. 3 CD79A
## 8 9.48e-271 3.59 0.622 0.022 1.30e-266 3 TCL1A
## 9 1.17e-178 2.97 0.957 0.241 1.60e-174 4 CCL5
## 10 4.93e-169 3.01 0.595 0.056 6.76e-165 4 GZMK
## 11 3.51e-184 3.31 0.975 0.134 4.82e-180 5 FCGR3A
## 12 2.03e-125 3.09 1 0.315 2.78e-121 5 LST1
## 13 1.05e-265 4.89 0.986 0.071 1.44e-261 6 GZMB
## 14 6.82e-175 4.92 0.958 0.135 9.36e-171 6 GNLY
## 15 1.48e-220 3.87 0.812 0.011 2.03e-216 7 FCER1A
## 16 1.67e- 21 2.87 1 0.513 2.28e- 17 7 HLA-DPB1
## 17 7.73e-200 7.24 1 0.01 1.06e-195 8 PF4
## 18 3.68e-110 8.58 1 0.024 5.05e-106 8 PPBPAdditional visualizations
VlnPlot(data, features = c("MS4A1", "CD79A"))
FeaturePlot(data, features = c("MS4A1", "CD79A"))
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.1.0 SeuratObject_4.0.0 Seurat_4.0.1 dplyr_1.0.2
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_2.0-0 deldir_0.2-3
## [4] ellipsis_0.3.1 ggridges_0.5.2 spatstat.data_2.1-0
## [7] leiden_0.3.5 listenv_0.8.0 farver_2.0.3
## [10] ggrepel_0.8.2 RSpectra_0.16-0 fansi_0.4.1
## [13] codetools_0.2-18 splines_4.0.2 knitr_1.30
## [16] polyclip_1.10-0 jsonlite_1.7.1 ica_1.0-2
## [19] cluster_2.1.0 png_0.1-7 uwot_0.1.10
## [22] shiny_1.5.0 sctransform_0.3.2 spatstat.sparse_2.0-0
## [25] compiler_4.0.2 httr_1.4.2 assertthat_0.2.1
## [28] Matrix_1.2-18 fastmap_1.0.1 lazyeval_0.2.2
## [31] limma_3.46.0 cli_2.1.0 later_1.1.0.1
## [34] htmltools_0.5.0 tools_4.0.2 igraph_1.2.6
## [37] gtable_0.3.0 glue_1.4.2 RANN_2.6.1
## [40] reshape2_1.4.4 Rcpp_1.0.5 scattermore_0.7
## [43] vctrs_0.3.4 nlme_3.1-150 lmtest_0.9-38
## [46] xfun_0.19 stringr_1.4.0 globals_0.13.1
## [49] mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0
## [52] irlba_2.3.3 goftest_1.2-2 future_1.20.1
## [55] MASS_7.3-53 zoo_1.8-8 scales_1.1.1
## [58] spatstat.core_2.0-0 promises_1.1.1 spatstat.utils_2.1-0
## [61] parallel_4.0.2 RColorBrewer_1.1-2 yaml_2.2.1
## [64] reticulate_1.18 pbapply_1.4-3 gridExtra_2.3
## [67] ggplot2_3.3.2 rpart_4.1-15 stringi_1.5.3
## [70] rlang_0.4.8 pkgconfig_2.0.3 matrixStats_0.57.0
## [73] evaluate_0.14 lattice_0.20-41 ROCR_1.0-11
## [76] purrr_0.3.4 tensor_1.5 htmlwidgets_1.5.2
## [79] labeling_0.4.2 cowplot_1.1.0 tidyselect_1.1.0
## [82] parallelly_1.21.0 RcppAnnoy_0.0.18 plyr_1.8.6
## [85] magrittr_1.5 R6_2.5.0 generics_0.1.0
## [88] DBI_1.1.0 pillar_1.4.6 withr_2.3.0
## [91] mgcv_1.8-33 fitdistrplus_1.1-1 survival_3.2-7
## [94] abind_1.4-5 tibble_3.0.4 future.apply_1.6.0
## [97] crayon_1.3.4 KernSmooth_2.23-18 utf8_1.1.4
## [100] spatstat.geom_2.0-1 plotly_4.9.2.1 rmarkdown_2.5
## [103] grid_4.0.2 data.table_1.13.2 digest_0.6.27
## [106] xtable_1.8-4 tidyr_1.1.2 httpuv_1.5.4
## [109] munsell_0.5.0 viridisLite_0.3.0